Ridgecv: Difference between revisions
Jump to navigation
Jump to search
imported>Jeremy (Importing text file) |
imported>Jeremy |
||
| (2 intermediate revisions by one other user not shown) | |||
| Line 1: | Line 1: | ||
===Purpose=== | ===Purpose=== | ||
| Line 6: | Line 5: | ||
===Synopsis=== | ===Synopsis=== | ||
:[b,theta,cumpress] = | :[b,theta,cumpress] = ridgecv(x,y,thetamax,divs,split) | ||
===Description=== | ===Description=== | ||
This function calculates a ridge regression model using a matching set of predictor variables (x-block) <tt>x</tt> and predicted variables (y-block) <tt>y</tt>, and uses cross-validation to determine the optimum value of the ridge parameter <tt>theta</tt>. The maximum value of the ridge parameter to consider is given by <tt>thetamax</tt> (where 0 < thetamax). | |||
====Inputs==== | |||
* '''x''' = matrix of independent variables | |||
* '''y''' = matching vector of dependent variables | |||
* '''thetamax''' = maximum value of ridge parameter <tt>theta</tt> to consider | |||
* '''divs''' = the number of values of <tt>theta</tt> to test | |||
* '''split''' = the number of times to split and test the data for cross-validation | |||
Outputs | ====Outputs==== | ||
* '''b''' = the regression column vector, at the optimal ridge parameter value | |||
* '''theta''' = the optimal ridge parameter value | |||
* '''cumpress''' = Predicted Residual Sum of Squares (PRESS) statistics for the cross-validation | |||
Note: RIDGECV uses the venetian blinds cross-validation method. | '''Note:''' RIDGECV uses the venetian blinds cross-validation method. | ||
===See Also=== | ===See Also=== | ||
[[crossval]], [[pcr]], [[pls]], [[analysis]], [[ridge]] | [[crossval]], [[pcr]], [[pls]], [[analysis]], [[ridge]] | ||
Latest revision as of 15:55, 22 February 2013
Purpose
Ridge regression with cross validation.
Synopsis
- [b,theta,cumpress] = ridgecv(x,y,thetamax,divs,split)
Description
This function calculates a ridge regression model using a matching set of predictor variables (x-block) x and predicted variables (y-block) y, and uses cross-validation to determine the optimum value of the ridge parameter theta. The maximum value of the ridge parameter to consider is given by thetamax (where 0 < thetamax).
Inputs
- x = matrix of independent variables
- y = matching vector of dependent variables
- thetamax = maximum value of ridge parameter theta to consider
- divs = the number of values of theta to test
- split = the number of times to split and test the data for cross-validation
Outputs
- b = the regression column vector, at the optimal ridge parameter value
- theta = the optimal ridge parameter value
- cumpress = Predicted Residual Sum of Squares (PRESS) statistics for the cross-validation
Note: RIDGECV uses the venetian blinds cross-validation method.